Flood is one of the natural disasters. It frequently causes costly economic losses and numbers of lives come to harm. Due to these severe injuries, how to perform an effective flood control is always a huge challenge for governments and water authorities. In order to establish successful flood control strategies to prevent or alleviate flood damages, besides implementing operating rules (regulations) established by water authorities for the hydraulic structures, applying real time optimization-based control strategies is a supplementary tool for water managers to make decisions. It is expected that the importance of such real time control strategies will become more important in the future. It is an ideal adaptation strategy in a world that is rapidly changing, for instance due to urbanization and climate trends. Real time control indeed allows making more efficient use of existing storage capacity available in flood control reservoirs. In order to accelerate the large number of iterations concerning the hydraulic computations in optimization procedures, a simplified river conceptual model was developed and connected to a Model Predictive Control (MPC) algorithm. This tool was applied to determine efficient real-time flood control policies for the 12 gated-weirs in the Belgian case study of the river Demer around two main flood control reservoirs. Because the system dynamics are nonlinear (gate openings are considered as inputs in the MPC), the MPC was combined with Genetic Algorithms (GAs) to cope with the nonlinear problems. The MPCGA model searches for better control actions by minimizing the cost function while at the same time avoiding violation of the defined constraints. The optimization results testify that MPCGA is capable of improving of the current regulation strategy that is based on fixed regulation rules and three-point controllers.
Chiang, Po-Kuan and Willems, Patrick, "Model Predictive Control Combined With Genetic Algorithms For A River System" (2014). CUNY Academic Works.